Atmos. Chem. Phys., 19, 13809–13825, 2019 https://doi.org/10.5194/acp-19-13809-2019 © Author(s) 2019. This work is distributed under the Creative Commons Attribution 4.0 License. Analysis of temporal and spatial variability of atmospheric CO2 concentration within Paris from the GreenLITE™ laser imaging experiment Jinghui Lian1, François-Marie Bréon1, Grégoire Broquet1, T. Scott Zaccheo2, Jeremy Dobler3,a, Michel Ramonet1, Johannes Staufer4,b, Diego Santaren1, Irène Xueref-Remy5,b, and Philippe Ciais1 1Laboratoire des Sciences du Climat et de l’Environnement (LSCE), IPSL, CEA-CNRS-UVSQ, Université Paris-Saclay, Gif-sur-Yvette, France 2Atmospheric and Environmental Research, Inc., Lexington, Massachusetts, USA 3Spectral Sensor Solutions LLC, Fort Wayne, Indiana, USA 4Thales, Labège, France 5Institut Méditerranéen de Biodiversité et d’Ecologie marine et continentale (IMBE), Aix Marseille Université, CNRS, IRD, Avignon Université, Aix-en-Provence, France aformerly at: Harris Corporation, Fort Wayne, Indiana, USA bformerly at: Laboratoire des Sciences du Climat et de l’Environnement (LSCE), IPSL, CEA-CNRS-UVSQ, Université Paris-Saclay, Gif-sur-Yvette, France Correspondence: Jinghui Lian ([email protected]) Received: 7 June 2019 – Discussion started: 4 July 2019 Revised: 2 October 2019 – Accepted: 11 October 2019 – Published: 18 November 2019 Abstract. In 2015, the Greenhouse gas Laser Imaging To- spatial differences in CO2 concentration that span the urban mography Experiment (GreenLITE™) measurement system area. However, within the city, there are larger discrepancies was deployed for a long-duration experiment in the cen- between the observations and the model results with very dis- ter of Paris, France. The system measures near-surface at- tinct features during winter and summer. During winter, the ™ mospheric CO2 concentrations integrated along 30 hori- GreenLITE measurements clearly demonstrate that one ur- zontal chords ranging in length from 2.3 to 5.2 km and ban canopy scheme (BEP) provides a much better descrip- 2 covering an area of 25 km over the complex urban envi- tion of temporal variations and horizontal differences in CO2 ronment. In this study, we use this observing system to- concentrations than the other (UCM) does. During summer, gether with six conventional in situ point measurements much larger CO2 horizontal differences are indicated by the and the Weather Research and Forecasting model cou- GreenLITE™ system than both the in situ measurements and pled with Chemistry (WRF-Chem) and two urban canopy the model results, with systematic east–west variations. schemes (Urban Canopy Model – UCM; Building Effect Pa- rameterization – BEP) at a horizontal resolution of 1 km to analyze the temporal and spatial variations in CO2 concen- trations within the city of Paris and its vicinity for the 1-year 1 Introduction period spanning December 2015 to November 2016. Such an analysis aims at supporting the development of CO2 atmo- Urban areas account for almost two-thirds of global energy spheric inversion systems at the city scale. Results show that consumption and more than 70 % of carbon emissions (IEA, both urban canopy schemes in the WRF-Chem model are ca- 2008). Human activities, such as fossil fuel burning (Duren pable of reproducing the seasonal cycle and most of the syn- and Miller, 2012) and cement production (Wang et al., 2012), optic variations in the atmospheric CO2 point measurements produce a net increase in atmospheric CO2 concentration over the suburban areas as well as the general corresponding within and downwind of the emission sources. Over the Published by Copernicus Publications on behalf of the European Geosciences Union. 13810 J. Lian et al.: Analysis of temporal and spatial variability of atmospheric CO2 concentration within Paris years, many instruments have been or will be used to mea- tween a laser-based transceiver and any given retroreflector. sure the urban atmospheric CO2 concentrations, including The path between a transceiver and a retroreflector is referred (i) ground-based monitoring networks in, e.g., Paris (Xueref- to as a “chord”. The GreenLITE™ system was developed Remy et al., 2018), Indianapolis (Davis et al., 2017), Los An- and deployed as part of several field campaigns over the past geles (Feng et al., 2016), Washington, DC (Mueller et al., several years (Dobler et al., 2013, 2017). These field tests 2018), Boston (Sargent et al., 2018); (ii) airborne campaigns have included extended operations at industrial facilities and conducted in, e.g., Colorado (Graven et al., 2009), London have shown that the system is capable of identifying and spa- (Font et al., 2015); (iii) existing space-based measurements, tially locating point sources of greenhouse gases (CO2 and 2 e.g., GOSAT (Hamazaki et al., 2004) and OCO-2 (Crisp et CH4) within a test area (∼ 1 km ). In conjunction with the al., 2008; Crisp, 2015); and (iv) future satellites with imaging 21st Conference of Parties to the United Nations Framework capabilities, e.g., OCO-3 (Elderling et al., 2019), GeoCarb Convention on Climate Change (COP 21), the GreenLITE™ (Moore et al., 2018), and CO2M (Buchwitz, 2018). These ob- system was deployed for a long-duration field test over cen- servations are used or could be used for estimating emissions tral Paris, France. The objective was to demonstrate the po- of CO2 over large cities using atmospheric inverse model- tential of CO2 concentration measurements along 30 hori- ing or to detect emission trends if these data are collected zontal chords ranging in length from 2.3 to 5.2 km and cover- over a sufficiently long period of time. High-accuracy con- ing an area of 25 km2. The aim of this field campaign was to ™ tinuous in situ ground-based measurements of CO2 concen- demonstrate the ability of GreenLITE to monitor the tem- trations, using the cavity ring-down spectroscopy (CRDS) poral and spatial variations in near-surface atmospheric CO2 technology, have been used in previous urban atmospheric concentrations over the complex urban environment. In addi- inversion studies for the quantification of CO2 emissions of tion, these measurements may be used for post-deployment large cities (Bréon et al., 2015; Staufer et al., 2016; Lauvaux analysis of the CO2 distribution with the ultimate goal of re- et al., 2016; Feng et al., 2016; Boon et al., 2016; Sargent et vealing the CO2 emission distribution. As a first step, the ob- al., 2018). However, many in situ stations may be needed to jectives of this work are to assess the information content ™ accurately capture the CO2 emission budget of a large city of the GreenLITE data, to analyze the atmospheric CO2 (Wu et al., 2016). Deploying such a network is expensive to distribution, and to characterize precisely the processes that install and maintain. The sparseness of CO2 concentration lead to dilution and mixing of the anthropogenic emissions, sampling sites limits the ability of inversions to estimate the which can provide new insights compared to the present in large spatial and temporal variations in the CO2 emissions situ point measurement approaches due to a much wider spa- within the city, even though high-resolution emission inven- tial coverage. ™ tories are available (e.g., AIRPARIF, 2013). The collection of the GreenLITE atmospheric CO2 New concepts and technologies are desirable for a full measurements in Paris makes it possible to evaluate and sampling of atmospheric CO2 concentrations within a city. potentially improve meteorological and atmospheric trans- These concepts may rely on moderate-precision but low-cost port models coupled to CO2 emission inventories. On the sensors that could be deployed at many sites for a high spatial other hand, the modeling system is expected to provide in- density sampling (Wu et al., 2016; Arzoumanian et al., 2019). terpretations of the temporal and spatial variations in the An alternative to in situ point measurements is a remote- GreenLITE™ data, with the aim of supporting the develop- sensing system based on the spectroscopic techniques which ment of CO2 atmospheric inversion systems at the city scale. ™ could provide long-path measurements of atmospheric trace Here we compare GreenLITE CO2 data with simulations gases over extended areas of interest. An example of this is performed with the Weather Research and Forecasting Model the differential optical absorption spectroscopy (DOAS). It coupled with a chemistry transport model (WRF-Chem). The has been applied to monitor atmospheric air pollution such WRF-Chem model allows various choices of physics pa- as nitrogen dioxide (NO2) and aerosol in a complex urban rameterizations and data assimilation methods for constrain- environment (Edner et al., 1993). A novel laser absorption ing the meteorological fields (Deng et al., 2017; Lian et al., spectroscopy based system for monitoring greenhouse gases 2018). Previous studies have shown that it is necessary to was developed by Spectral Sensor Solutions LLC and Atmo- account for specific urban effects when modeling the trans- spheric and Environmental Research, Inc. (AER). This sys- port and dispersion of CO2 over complex urban areas such tem, known as the Greenhouse gas Laser Imaging Tomogra- as Salt Lake City, UT, and Los Angeles, CA (Nehrkorn et al., phy Experiment (GreenLITE™), consists of a set of continu- 2013; Feng et al., 2016). Nevertheless, even when the urban ously operating laser-based transceivers and a set of retrore- environment is accounted for, the modeling of atmospheric flectors separated by a few kilometers. Both data collection transport is a challenge. Significant mismatches remain be- and data processing components are based on the intensity- tween modeled and measured concentrations that could be modulated continuous-wave (IM-CW) measurement tech- explained by transport biases, particularly at night, and verti- nique, which is described in detail in Dobler et al. (2017). cal mixing during the day. This instrument provides estimates of the average CO2 con- In this study, we present the results from a set of 1- centrations along the line of sight defined by the path be- year simulations (from December 2015 to November 2016) Atmos.